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Semantic hashing has become a crucial component of fast similarity search inmany large-scale information retrieval systems, in particular, for text data.Variational auto-encoders (VAEs) with binary latent variables as hashing codesprovide state-of-the-art performance in terms of precision for documentretrieval. We propose a pairwise loss function with discrete latent VAE toreward within-class similarity and between-class dissimilarity for supervisedhashing. Instead of solving the optimization relying on existing biasedgradient estimators, an unbiased low-variance gradient estimator is adopted tooptimize the hashing function by evaluating the non-differentiable lossfunction over two correlated sets of binary hashing codes to control thevariance of gradient estimates. This new semantic hashing framework achievessuperior performance compared to the state-of-the-arts, as demonstrated by ourcomprehensive experiments.

Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator